CN113593225A - Single-point intersection vehicle control method oriented to pure internet environment - Google Patents

Single-point intersection vehicle control method oriented to pure internet environment Download PDF

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CN113593225A
CN113593225A CN202110825811.5A CN202110825811A CN113593225A CN 113593225 A CN113593225 A CN 113593225A CN 202110825811 A CN202110825811 A CN 202110825811A CN 113593225 A CN113593225 A CN 113593225A
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CN113593225B (en
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马万经
刘圣月
俞春辉
王玲
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Tongji University
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Abstract

The invention relates to a single-point intersection vehicle control method for a pure internet environment, which specifically comprises the following steps: s1, collecting physical state information of the networked automatic driving vehicle in the intersection control area; s2, determining the selection range of the acceleration strategy and the lane strategy of the vehicle end; s3, establishing the income of the individual vehicle; s4, establishing a alliance characteristic function of the vehicle group; s5, establishing an intersection internet automatic driving vehicle cooperative driving model based on a cooperative game theory; s6, calculating to obtain the optimal driving strategy combination of the vehicle group, and sending the optimal driving strategy combination to the corresponding vehicle end; and S7, the vehicle end sends a control command to adjust the driving behavior according to the received optimal driving strategy. Compared with the prior art, the method has the advantages of effectively improving the comprehensive benefits of vehicle groups in the aspects of safety, efficiency and comfort, practically providing decision-making suggestions for the process of the networked automatic driving vehicles passing through the intersection and the like.

Description

Single-point intersection vehicle control method oriented to pure internet environment
Technical Field
The invention relates to the technical field of vehicle control at road intersections, in particular to a single-point intersection vehicle control method oriented to a pure internet environment.
Background
In recent years, the quantity of motor vehicles owned by people is remarkably increased, the traffic transportation demand is increased, the load of a road traffic system is more serious, traffic accidents are frequent, the phenomenon of vehicle congestion is prominent, and the problem of environmental pollution is increasingly serious. The incidence of the behaviors of starting, stopping, accelerating and decelerating and the like of the vehicles in the intersection area is obviously higher than that of other road network areas, and the negative influence is caused on the overall consistency of the traffic flow operation, so that traffic delay is generated. The reasonable control of the vehicle running at the intersection ensures the safety of the vehicle at the intersection and simultaneously ensures the driving efficiency and the comfort of passengers, which is the basis for solving the road traffic problem.
The current main development direction of the intelligent traffic system is the automatic driving intelligent vehicle-road cooperation technology, the development center of gravity of the intelligent traffic system is transited to the process of group intelligence and environment intelligence interaction from a single intelligence stage, advanced wireless communication and internet technologies provide guarantee for interconnection and intercommunication among automatic driving vehicles, and the information sharing degree among the automatic driving vehicles is greatly improved. With the continuous development of artificial intelligence technology and the automotive field, autonomous vehicles have been in five grades. The development of the automatic driving technology provides possibility for vehicle group control, and the aim of comprehensively regulating and controlling a vehicle system is fulfilled from the global benefit, so that the automatic driving technology plays an important role in improving the control effect of the whole traffic system.
At present, corresponding control methods are available at home and abroad, but the research on single individual vehicles is mostly aimed at, the vehicles are independent from each other, and the research on cooperative behaviors in the driving process of the vehicles is lacked; the traffic environment is mostly simplified into a single lane, and the lane changing behavior of vehicles in the real environment is ignored.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a vehicle control method for a single-point intersection in a pure internet environment, so that the comprehensive performance of the vehicle on three aspects of safety, efficiency and comfort when the vehicle passes through the intersection is effectively improved.
The purpose of the invention can be realized by the following technical scheme:
a single intersection vehicle control method for a pure internet environment specifically comprises the following steps:
s1, collecting physical state information of the networked automatic driving vehicle in the intersection control area;
s2, determining an acceleration strategy of the vehicle end and a selection range of a lane strategy according to the physical state information;
s3, establishing the income of the individual vehicle by combining the acceleration strategy and the lane strategy of the vehicle end;
s4, establishing a alliance characteristic function of the vehicle group based on the income of the individual vehicle;
s5, establishing an intersection internet automatic driving vehicle cooperative driving model based on a cooperative game theory according to the alliance characteristic function of the vehicle group;
s6, calculating to obtain the optimal driving strategy combination of the vehicle group according to the intersection internet automatic driving vehicle cooperative driving model, and sending the optimal driving strategy combination to the corresponding vehicle end;
and S7, the vehicle end sends a control command to adjust the driving behavior according to the received optimal driving strategy.
The range of the intersection control area is specifically within the range from the preset distance Lc before all the entrance lane stop lines of the intersection to the position of the exit intersection.
The physical state information includes two-dimensional spatial position, velocity, and acceleration.
The acceleration strategy of the vehicle end is based on the speed of the vehicle end at the current moment, and the specific formula is as follows:
amin≤ai≤amax
Figure BDA0003173756140000021
wherein, aiIs the acceleration of the vehicle end i, aminIs the minimum acceleration of the vehicle end, amaxIs the maximum acceleration, v, of the vehicle endiIs the speed, v, of the vehicle end iminIs the most important vehicle endSmall velocity, vmaxIs the maximum speed at the vehicle end and Δ t is the unit time.
The benefits of the individual vehicle include safety benefits, efficiency benefits, and comfort benefits.
Further, the safety benefit is an opposite number of the driving risk value, the driving risk value is calculated according to a driving potential energy field formed by the movement of the motor vehicle, and a specific formula is as follows:
Figure BDA0003173756140000022
Ui_safe＝∑Uij_safe,j∈ω(i)
wherein, Uij_safeFor the safety gains of vehicle end i in the driving situation of vehicle end j, EV_joThe potential danger degree, namely the driving risk value U, generated at the conflict point o for the moving object corresponding to the vehicle end ji_safeFor the total safety gain of vehicle end i, MjFor the equivalent mass of vehicle end j, G, k1And k2Are all constant coefficients greater than 0, L'jIs the length of a moving object corresponding to the vehicle end j, omega (i) is the set of other vehicle ends when the vehicle end i enters the intersection control area, thetajThe motion direction and the vector r of the moving object corresponding to the vehicle end jjoAngle of (a) ofj∈[-π,π]Clockwise is positive, rjoThe vector of the vehicle end j pointing to the conflict point o is the same as the field intensity direction, the field intensity changes fastest in the direction, | rjoL is the distance between the vehicle end j and the conflict point o, v'jThe speed of the moving object corresponding to the vehicle end j.
Further, the efficiency gain is specifically calculated by the speed change of the vehicle end in unit time, and the specific formula is as follows:
Figure BDA0003173756140000031
wherein, Ui_effFor the efficiency gain of the vehicle end i,
Figure BDA0003173756140000032
the speed of the vehicle end i at the time t +1,
Figure BDA0003173756140000033
is the speed of the vehicle end i at the time t, Δ t is the unit time, aiIs the acceleration of the vehicle end i.
Further, the evaluation index of the comfort benefit includes longitudinal acceleration change information of the vehicle and transverse accumulated lane change times, and the specific formula is as follows:
Ui_com＝-|ai t+1-ai t|-ci t+1
wherein, Ui_comFor the comfort benefit of the vehicle end i, ai t+1Acceleration of the vehicle end i at time t +1, ai tIs the speed of the vehicle end i at time t, ci t+1Is the accumulated lane change number of the vehicle terminal i from the initial time to the time t + 1.
Further, the calculation formula of the accumulated lane change times of the vehicle end i from the initial time to the time t +1 is as follows:
Figure BDA0003173756140000034
wherein, ci tTo accumulate the number of lane changes at the vehicle end i from the initial time to time t, cmaxAnd the maximum value of the lane changing times of the vehicle end is obtained.
After the safety benefits, the efficiency benefits and the comfort benefits of the individual vehicles are subjected to normalization processing, the benefits of the individual vehicles are calculated, and the specific formula is as follows:
Ui＝αU′i_safe+βU′i_eff+γU′i_com
α+β+γ＝1
wherein, α, β andgamma is weight coefficient of safety benefit, efficiency benefit and comfort benefit respectively, and is real number between 0 and 1, U'i_safe、U'i_effAnd U'i_comRespectively the security gain, efficiency gain and comfort gain after normalization.
The vehicle alliance characteristic function is calculated based on the maximum and minimum criteria, and a driver can take the worst driving environment into consideration for ensuring driving safety and then make a decision under the condition that the driver does not determine which strategy is taken by other vehicles, wherein the specific formula is as follows:
Figure BDA0003173756140000041
Figure BDA0003173756140000042
Figure BDA0003173756140000043
wherein S is a vehicle alliance, S1,s2,…,snN is the largest vehicle group in the vehicle alliance, and v(s) is an alliance feature function of the vehicle groups in the cooperative game.
The intersection internet automatic driving vehicle cooperative driving model is based on continuous time.
The optimal driving strategy comprises an acceleration adjusting strategy and a lane adjusting strategy.
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the prior invention which takes the time difference of vehicle collision, namely the time interval of the vehicles with potential collision passing through the conflict point in sequence as the trigger condition, the invention can realize better control effect because the former does not take any measures for the vehicles meeting the condition of the time difference of safety collision, and the vehicles in the invention can improve the traffic efficiency by further improving the current strategy on the premise of ensuring the safety.
2. The invention considers the synchronous optimization of the vehicle in the aspects of an acceleration strategy and a lane strategy, combines the practical conditions of the intersection, has good practicability, improves the operability space of the vehicle, ensures the comprehensive income of the vehicle and better accords with the real driving environment compared with the prior invention only considering the acceleration.
3. The invention realizes different emphasis on safety, efficiency and comfort in the driving process of the vehicle end by adjusting the parameters of the earnings of the individual vehicles, is suitable for vehicles with different requirements and has strong flexibility.
4. The method and the system fully consider the requirements of the networked automatic driving vehicle on safety, efficiency and comfort in the actual running process from the perspective of global benefits, realize the effect under the optimal control of the vehicle group, and can better serve the management and control of the intelligent networked vehicle.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic illustration of the intersection control range of the present invention;
fig. 3 is a schematic diagram of an application in an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The present embodiment is based on the following assumptions:
1) in the embodiment, the vehicle-to-vehicle communication and vehicle-to-road communication can be realized by all vehicles, and required information can be transmitted through the vehicle-to-vehicle communication and the vehicle-to-road communication, wherein the permeability of a network-connected automatic driven vehicle (CAV) is 100 percent in the environment of the Internet of vehicles;
2) the steering behavior of the vehicle at the intersection is not considered;
3) pedestrian and non-motor vehicle interference is not considered;
4) the vehicle can complete the lane changing process instantly and safely;
5) the time delay of vehicle-to-vehicle and vehicle-to-road communication is not considered, the speed of information transmission and processing is fast enough compared with the speed of a vehicle, the communication and vehicle control can meet the requirement of automatic driving, and the automatic driving technology can be applied in a mature mode.
In this embodiment, the driving state and the driving environment of the networked autonomous vehicles are known, the driving state and the like of each autonomous vehicle can be acquired by other vehicles, parameters including the real-time distance, the speed and the distance from the potential conflict point between each autonomous vehicle can be acquired, and high-quality information interaction between the two autonomous vehicles can be realized.
As shown in fig. 1, a single intersection vehicle control method for a pure internet environment specifically includes the following steps:
s1, collecting physical state information of the networked automatic driving vehicle in the intersection control area;
s2, determining the acceleration strategy of the vehicle end and the selection range of the lane strategy according to the physical state information;
s3, establishing the income of the individual vehicle by combining the acceleration strategy and the lane strategy of the vehicle end;
s4, establishing a alliance characteristic function of a vehicle group based on the maximum and minimum criteria based on the income of the individual vehicle;
s5, establishing an intersection internet automatic driving vehicle cooperative driving model based on a cooperative game theory according to the alliance characteristic function of the vehicle group;
s6, calculating to obtain the optimal driving strategy combination of the vehicle group according to the intersection internet automatic driving vehicle cooperative driving model, and sending the optimal driving strategy combination to the corresponding vehicle end;
and S7, the vehicle end sends a control command to adjust the driving behavior according to the received optimal driving strategy.
As shown in fig. 2, the range of the intersection control area is specifically within a range from a preset distance Lc before all the entrance lane stop lines of the intersection to the position of exiting the intersection.
In this embodiment, if only one vehicle end is located in the intersection control area, the vehicle control is not performed, and the vehicle continues to run according to the existing strategy.
In step S2, as shown in fig. 3, in terms of acceleration, the vehicle end may select one from the acceleration set as the current strategy; on the lane side, the vehicle end can select all lanes of the entrance lane.
The physical state information includes two-dimensional spatial position, velocity, and acceleration.
The acceleration strategy of the vehicle end is based on the speed of the vehicle end at the current moment, and the specific formula is as follows:
amin≤ai≤amax
Figure BDA0003173756140000061
wherein, aiIs the acceleration of the vehicle end i, aminIs the minimum acceleration of the vehicle end, amaxIs the maximum acceleration, v, of the vehicle endiIs the speed, v, of the vehicle end iminIs the minimum speed, v, at the vehicle endmaxIs the maximum speed at the vehicle end and Δ t is the unit time.
The speed of the vehicle end after the acceleration strategy is implemented is in accordance with the speed limit, and if the speed of the vehicle end is smaller than the maximum speed and larger than zero, the vehicle end can select a proper forward acceleration; when the vehicle end is running at maximum speed, only deceleration or keeping constant speed can be selected.
The benefits of an individual vehicle include safety benefits, efficiency benefits, and comfort benefits.
The safety benefit is the opposite number of the driving risk value, the driving risk value is obtained by taking a kinetic energy field formed by the movement of the motor vehicle as a driving potential energy field and calculating according to the driving potential energy field, and the specific formula is as follows:
Figure BDA0003173756140000071
Ui_safe＝∑Uij_safe,j∈ω(i)
wherein, Uij_safeFor the safety gains of vehicle end i in the driving situation of vehicle end j, EV_joThe potential danger degree, namely the driving risk value U, generated at the conflict point o for the moving object corresponding to the vehicle end ji_safeFor the total safety gain of vehicle end i, MjFor the equivalent mass of vehicle end j, G, k1And k2Are all constant coefficients greater than 0, L'jIs the length of a moving object corresponding to the vehicle end j, omega (i) is the set of other vehicle ends when the vehicle end i enters the intersection control area, thetajThe motion direction and the vector r of the moving object corresponding to the vehicle end jjoAngle of (a) ofj∈[-π,π]Clockwise is positive, rjoThe vector of the vehicle end j pointing to the conflict point o is the same as the field intensity direction, the field intensity changes fastest in the direction, | rjoL is the distance between the vehicle end j and the conflict point o, v'jThe speed of the moving object corresponding to the vehicle end j.
The efficiency gain is specifically calculated by the speed change of the vehicle end in unit time, and the specific formula is as follows:
Figure BDA0003173756140000072
wherein, Ui_effFor the efficiency gain of the vehicle end i,
Figure BDA0003173756140000073
the speed of the vehicle end i at the time t +1,
Figure BDA0003173756140000074
is the speed of the vehicle end i at the time t, Δ t is the unit time, aiIs the acceleration of the vehicle end i.
The evaluation indexes of the comfort benefit comprise longitudinal acceleration change information of the vehicle and transverse accumulated lane change times, the comfort benefit is increased by smaller vehicle acceleration change and fewer lane change times, and the specific formula is as follows:
Ui_com＝-|ai t+1-ai t|-ci t+1
wherein, Ui_comFor the comfort benefit of the vehicle end i, ai t+1Acceleration of the vehicle end i at time t +1, ai tIs the speed of the vehicle end i at time t, ci t+1Is the accumulated lane change number of the vehicle terminal i from the initial time to the time t + 1.
The calculation formula of the accumulated lane change times of the vehicle end i from the initial time to the time t +1 is as follows:
Figure BDA0003173756140000075
wherein, ci tTo accumulate the number of lane changes at the vehicle end i from the initial time to time t, cmaxAnd the maximum value of the lane changing times of the vehicle end is obtained.
After the safety benefits, the efficiency benefits and the comfort benefits of the individual vehicles are subjected to normalization processing, the benefits of the individual vehicles are calculated, and the specific formula is as follows:
Ui＝αU′i_safe+βU′i_eff+γU′i_com
α+β+γ＝1
wherein alpha, beta and gamma are weight coefficients of safety benefit, efficiency benefit and comfort benefit respectively, and are real numbers between 0 and 1 and U'i_safe、U'i_effAnd U'i_comRespectively the security gain, efficiency gain and comfort gain after normalization.
The vehicle alliance characteristic function is calculated based on the maximum and minimum criteria, and a driver can take the worst driving environment possibly encountered into consideration and then make a decision for ensuring the driving safety under the condition that the driver does not determine what strategy is adopted by other vehicles, wherein the specific formula is as follows:
Figure BDA0003173756140000081
Figure BDA0003173756140000082
Figure BDA0003173756140000083
wherein S is a vehicle alliance, S1,s2,…,snN is the largest vehicle group in the vehicle alliance, and v(s) is an alliance feature function of the vehicle groups in the cooperative game.
The intersection internet automatic driving vehicle cooperative driving model is based on continuous time.
In the step S6, a genetic algorithm is used as a solving algorithm to calculate the optimal driving strategy combination of the vehicle group, the specific process is that an initial group is randomly generated, and one chromosome contains the acceleration strategies and the lane selection strategies of all vehicle ends in the model. For each vehicle end, the speed limit is satisfied at [ a ]min,amax]In the embodiment, an integer coding mode is adopted, when n vehicles exist in an intersection control area, the number of the vehicles is counted from left to right, 1 to n positions of the chromosomes represent the acceleration of n vehicle ends respectively, and n +1 to 2n positions represent the lanes selected by the n vehicle ends respectively. The coded value of the acceleration bit represents the acceleration strategy value of the vehicle end, and the coded value of the lane bit represents the lane number selected by the vehicle end. And taking the alliance characteristic function of the vehicle end as the fitness of the chromosome, and iterating through selection, intersection and variation loops until the optimal strategy combination of the vehicle end is obtained.
The optimal driving strategy comprises an acceleration adjustment strategy and a lane adjustment strategy.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.

Claims (10)

1. A single intersection vehicle control method for a pure internet environment is characterized by comprising the following steps:
s1, collecting physical state information of the networked automatic driving vehicle in the intersection control area;
s2, determining an acceleration strategy of the vehicle end and a selection range of a lane strategy according to the physical state information;
s3, establishing the income of the individual vehicle by combining the acceleration strategy and the lane strategy of the vehicle end;
s4, establishing a alliance characteristic function of the vehicle group based on the income of the individual vehicle;
s5, establishing an intersection internet automatic driving vehicle cooperative driving model based on a cooperative game theory according to the alliance characteristic function of the vehicle group;
s6, calculating to obtain the optimal driving strategy combination of the vehicle group according to the intersection internet automatic driving vehicle cooperative driving model, and sending the optimal driving strategy combination to the corresponding vehicle end;
and S7, the vehicle end sends a control command to adjust the driving behavior according to the received optimal driving strategy.
2. The pure internet environment-oriented single intersection vehicle control method according to claim 1, wherein the physical state information comprises two-dimensional spatial position, speed and acceleration.
3. The single intersection vehicle control method oriented to the pure internet environment is characterized in that the acceleration strategy of the vehicle end is based on the speed of the vehicle end at the current moment, and the specific formula is as follows:
amin≤ai≤amax
Figure FDA0003173756130000011
wherein, aiIs the acceleration of the vehicle end i, aminIs the minimum acceleration of the vehicle end, amaxIs the maximum acceleration, v, of the vehicle endiIs the speed, v, of the vehicle end iminIs the minimum speed, v, at the vehicle endmaxIs the maximum speed at the vehicle end and Δ t is the unit time.
4. The pure internet environment-oriented single intersection vehicle control method of claim 1, wherein the benefits of the individual vehicle include safety benefits, efficiency benefits, and comfort benefits.
5. The pure internet environment-oriented single intersection vehicle control method according to claim 4, wherein the safety gains are opposite numbers of driving risk values, the driving risk values are obtained by taking a kinetic energy field formed by movement of a motor vehicle as a driving potential energy field and calculating according to the driving potential energy field, and a specific formula is as follows:
Figure FDA0003173756130000021
Ui_safe＝∑Uij_safe,j∈ω(i)
wherein, Uij_safeFor the safety gains of vehicle end i in the driving situation of vehicle end j, EV_joThe potential danger degree generated at the conflict point o by the moving object corresponding to the vehicle end j,i.e. driving risk value, Ui_safeFor the total safety gain of vehicle end i, MjFor the equivalent mass of vehicle end j, G, k1And k2Are all constant coefficients greater than 0, L'jIs the length of a moving object corresponding to the vehicle end j, omega (i) is the set of other vehicle ends when the vehicle end i enters the intersection control area, thetajThe motion direction and the vector r of the moving object corresponding to the vehicle end jjoAngle of (a) ofj∈[-π,π]Clockwise is positive, rjoThe vector of the vehicle end j pointing to the conflict point o is the same as the field intensity direction, the field intensity changes fastest in the direction, | rjoL is the distance between the vehicle end j and the conflict point o, v'jThe speed of the moving object corresponding to the vehicle end j.
6. The pure internet environment-oriented single intersection vehicle control method according to claim 4, wherein the efficiency gain is calculated by a speed change of a vehicle end in unit time, and a specific formula is as follows:
Figure FDA0003173756130000022
wherein, Ui_effFor the efficiency gain of the vehicle end i,
Figure FDA0003173756130000023
the speed of the vehicle end i at the time t +1,
Figure FDA0003173756130000024
is the speed of the vehicle end i at the time t, Δ t is the unit time, aiIs the acceleration of the vehicle end i.
7. The pure internet environment-oriented single intersection vehicle control method according to claim 4, wherein the comfort benefit evaluation index comprises longitudinal acceleration change information and transverse accumulated lane change times of the vehicle, and a specific formula is as follows:
Figure FDA0003173756130000025
wherein, Ui_comFor the comfort benefit of the vehicle end i, ai t+1Acceleration of the vehicle end i at time t +1, ai tIs the speed of the vehicle end i at time t, ci t+1Is the accumulated lane change number of the vehicle terminal i from the initial time to the time t + 1.
8. The single intersection vehicle control method oriented to the pure internet environment according to claim 7, wherein the calculation formula of the accumulated lane change times of the vehicle end i from the initial time to the t +1 time is as follows:
Figure FDA0003173756130000031
wherein, ci tTo accumulate the number of lane changes at the vehicle end i from the initial time to time t, cmaxAnd the maximum value of the lane changing times of the vehicle end is obtained.
9. The pure internet environment-oriented single intersection vehicle control method according to claim 4, characterized in that after normalization processing is performed on safety gains, efficiency gains and comfort gains of the individual vehicles, gains of the individual vehicles are calculated, and a specific formula is as follows:
Ui＝αU′i_safe+βU′i_eff+γU′i_com
α+β+γ＝1
wherein alpha, beta and gamma are weight coefficients of safety benefit, efficiency benefit and comfort benefit respectively, and are real numbers between 0 and 1 and U'i_safe、U'i_effAnd U'i_comRespectively the security gain, efficiency gain and comfort gain after normalization.
10. The pure internet environment-oriented single intersection vehicle control method according to claim 1, characterized in that the optimal driving strategy comprises an acceleration regulation strategy and a lane regulation strategy.
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